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Building digital trust: The role of data ethics in the digital age

Overview

The digital economy is built on data—massive streams of data being created, collected, combined and shared—for which traditional governance frameworks and risk-mitigation strategies are insufficient. In the digital age, analyzing and acting on insights from data can introduce entirely new classes of risk. These include unethical or even illegal use of insights, amplifying biases that exacerbate issues of social and economic justice, and using data for purposes to which its original disclosers would not have agreed, and without their consent. These and other practices can permanently damage consumer trust in a brand.

In the past, the scope for digital risk was limited to cybersecurity threats but leading organizations must now also recognize risks from lackluster ethical data practices. Mitigating these internal threats is critical for every player in the digital economy, and cannot be addressed with strong cybersecurity alone.

Accenture Labs launched a research collaboration with leading thinkers on data ethics to help provide guidelines for security executives and data practitioners and enable development of robust ethical controls throughout data supply chains.

Data Ethics Across the Supply Chain

In the digital era, data is the fundamental currency. How organizations handle it throughout the data supply chain—from collection, aggregation, sharing and analysis, to monetization, storage and disposal—can have a decisive impact on their reputation and effectiveness. A data supply chain framework helps practitioners evaluate current ethical practices and implement appropriate ethical controls at each step.

Developing a Code of Data Ethics

A set of universal principles of data ethics can help guide data science professionals and practitioners in creating a code of data ethics that is specific and contextual for their organization or community of stakeholders:

1. The highest priority is to respect the persons behind the data.

Where insights derived from data could impact the human condition, the potential harm to individuals and communities should be the paramount consideration. Big data can produce compelling insights into populations, but those same insights can be used to unfairly limit an individual’s possibilities.

2. Account for the downstream uses of datasets.

Data professionals should strive to use data in ways that are consistent with the intentions and understanding of the disclosing party. Many regulations govern datasets on the basis of the status of the data: “public,” “private” or "proprietary," for example. But what is done with datasets is ultimately more consequential to subjects/users than the type of data or the context in which it is collected. Correlative use of repurposed data in research and industry represents the greatest promise and the greatest risk of data analytics.

3. The consequences of utilizing data and analytical tools today are shaped by how they’ve been used in the past.

There’s no such thing as raw data. All datasets and accompanying analytic tools carry a history of human decision-making. As far as possible, that history should be auditable. This should include mechanisms for tracking the context of collection, methods of consent, chains of responsibility, and assessments of data quality and accuracy.

A set of universal principles of data ethics can help guide data science professionals and practitioners in creating a code of data ethics that is specific and contextual for their organization or community of stakeholders:

4. Seek to match privacy and security safeguards with privacy and security expectations.

Data subjects hold a range of expectations about the privacy and security of their data. These expectations are often context-dependent. Designers and data professionals should give due consideration to those expectations and align safeguards and expectations with them, as much as possible.

5. Always follow the law, but understand that the law is often a minimum bar.

Digital transformations have become a standard evolutionary path for businesses and governments. However, because laws have largely failed to keep up with the pace of digital innovation and change, existing regulations are often miscalibrated to current risks. In this context, compliance means complacency. To excel in data ethics, leaders must define their own compliance frameworks to outperform legislated requirements.

6. Be wary of collecting data just for the sake of having more data.

The power and peril of data analytics is that data collected today will be useful for unpredictable purposes in the future. Give due consideration to the possibility that less data may result in both better analysis and less risk.

A set of universal principles of data ethics can help guide data science professionals and practitioners in creating a code of data ethics that is specific and contextual for their organization or community of stakeholders:

7. Data can be a tool of both inclusion and exclusion.

While everyone should have access to the social and economic benefits of data, not everyone is equally impacted by the processes of data collection, correlation, and prediction. Data professionals should strive to mitigate the disparate impacts of their products and listen to the concerns of affected communities.

8. As far as possible, explain methods for analysis and marketing to data disclosers.

Maximizing transparency at the point of data collection can minimize the more significant risks that arise as data travels through the data supply chain.

9. Data scientists and practitioners should accurately represent their qualifications (and limits to their expertise), adhere to professional standards, and strive for peer accountability.

The long-term success of this discipline depends on public and client trust. Data professionals should develop practices for holding themselves and their peers accountable to shared standards.

A set of universal principles of data ethics can help guide data science professionals and practitioners in creating a code of data ethics that is specific and contextual for their organization or community of stakeholders:

Not all ethical dilemmas have design solutions. But paying close attention to design practices can break down many of the practical barriers that stand in the way of shared, robust ethical standards. Data ethics is an engineering challenge worthy of the best minds in the field.

11. Products and research practices should be subject to internal (and potentially external) ethical review.

Organizations should prioritize establishing consistent, efficient, and actionable ethics review practices for new products, services, and research programs. Internal peer-review practices help to mitigate risk, and an external review board can contribute significantly to public trust.

12. Governance practices should be robust, known to all team members and regularly reviewed.

Guiding Ethical Decisions

How can organizations design and reinforce ethical decision-making across the data supply chain? A data ethics framework helps guide ethical reviews at each step of the supply chain. Ethical design reviews can fit seamlessly into existing best practices for project management and service design:

Informed Consent and Avoiding Harm

Trust can be improved at the beginning of a data supply chain by making informed consent a priority. Where do you start? Explore guidelines for avoiding harm across data disclosure, manipulation and consumption:

Data Disclosure

DATA AT REST

Data may be sourced from archives or other backups

Guideline: Ensure the context of original consent is known and respected; data security practices should be revisited on a regular basis to minimize risk of accidental disclosure. Aggregation of data from multiple sources often represents a new context for disclosure; have the responsible parties made a meaningful effort to renew informed consent agreements for this new context?

DATA IN MOTION

Data is collected in real-time from machine sensors, automated processes, or human input; while in motion, data may or may not be retained, reshaped, corrupted, disclosed, etc.

Guideline: Be respectful of data disclosers and the individuals behind the data. Protect the integrity and security of data throughout networks and supply chains. Only collect the minimum amount of data needed for a specific application. Avoid collecting personally identifiable information, or any associated meta-data whenever possible. Maximize preservation of provenance.

Trust can be improved at the beginning of a data supply chain by making informed consent a priority. Where do you start? Explore guidelines for avoiding harm across data disclosure, manipulation and consumption:

Data Manipulation

Guideline: Set up a secure environment for handling static data so the risk of security breaches is minimized and data is not mistakenly shared with external networks. Data movement and transformation should be fully auditable

DATA IN MOTION

Data is actively being moved or aggregated; data transformations use multiple datasets or API calls which might be from multiple parties; the Internet may be used

Guideline: Ensure that data moving between networks and cloud service providers is encrypted; shared datasets should strive to minimize the amount of data shared and anonymize as much as possible. Be sure to destroy any temporary databases that contain aggregated data. Are research outcomes consistent with the discloser’s original intentions?

Trust can be improved at the beginning of a data supply chain by making informed consent a priority. Where do you start? Explore guidelines for avoiding harm across data disclosure, manipulation and consumption:

Data Consumption

DATA AT REST

Data analytics processes do not rely on live or real-time updates

Guideline: Consider how comfortable data disclosers would be with how the derived insights are being applied. Gain consent, preferably informed consent, from data disclosers for application-specific uses of data.

DATA IN MOTION

Data insights could be context-aware, informed by sensors, or might benefit from streamed data or API calls

Guideline: The data at rest guidelines for data consumption are equally important here. In addition, adhere to any license agreements associated with the APIs being used. Encrypt data. Be conscious of the lack of control over streamed data once it is broadcast. Streaming data also has a unique range of potential harms—the ability to track individuals, deciphering network vulnerabilities, etc.

Ethical Algorithms and Automation

New risks and challenges in the digital economy extend to various types of automation that are powered by data insights. Sense and respond (S&R) systems have been in use for decades, responding to their environment in real time with little or no human input.

As the prevalence and decision-making capabilities of S&R systems continue to increase, there’s potential for ethical failures with wider impact. If ethics are not properly considered during S&R design, implementation, and use, they can propagate unwanted biases and erode human trust in both the systems and the organizations that deploy them. Practitioners designing S&R systems that will become a key part of consumer life must take these risks into account.

GUIDELINES FOR DATA SHARING

Data is the lifeblood of the digital economy, and data sharing has become an essential practice, enabling new insights as broader business ecosystems propagate. Yet, new risks loom that require attention from the leaders of any organization that engages in data sharing, aggregating or analytics. We examine a best-practice approach for data sharing to ensure ethics are properly considered and risks are appropriately identified and mitigated.

Conclusion

New vectors of risk are scattered throughout the data supply chain. How businesses, governments and NGOs address this risk is critical to their ability to operate. As ethical data concerns grow, organizations need to find a new way forward, and should embrace the opportunity: This new ethical frontier offers a way to engender trust and provide vital differentiation in a crowded marketplace. Organizations should reduce their exposure to digital risk by integrating a wide array of data ethics practices throughout their data supply chains. In doing so, they’ll gain the trust of stakeholders, reap business benefits and position themselves for prolonged success.

Data Ethics Research Initiative

Launched by Accenture’s Technology Vision team, the Data Ethics Research Initiative brings together leading thinkers and researchers from Accenture Labs and over a dozen external organizations to explore the most pertinent issues of data ethics in the digital economy. The goal of this research initiative is to outline strategic guidelines and tactical actions businesses, government agencies and NGOs can take to adopt ethical practices throughout their data supply chains.

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